Error in Patient Care and Clinical Practice drug allergy

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Slide 1 : Error in Patient Care and Clinical Practice James Harrison, M.D., Ph.D. Director of Clinical Informatics Department of Public Health Sciences University of Virginia james.harrison@virginia.edu
Slide 2 : Medical Error Agenda Understanding error in industry and healthcare Types and sources of human error Decreasing the potential for error How information systems can help Can information systems be harmful?
Slide 3 : Previous Work in Error from Industry Industrial, nuclear, airline, NASA accidents Bad outcomes occur through interaction of human, system and organizational errors (unpredictable) Tightly-coupled vs. loosely-coupled systems Importance of problem-reporting and experience Progressive improvement through sustained effort 99% error-free is not adequate (current industrial goal is 3 errors per 1 million actions, 99.9997%) Financial incentives are important in reducing error Impressive success over 30 years
Slide 4 : Focus of Attention on Medical Error Quality of Healthcare in America project Endowed by Howard Hughes Medical Institute within the Institute of Medicine Committee composed of 38 leaders from academia, health care industry, government, other industries (e.g., aviation) Review of research on medical error initiated June 1998 To Err is Human: Building a Safer Health System issued in 1999 http://books.nap.edu/books/0309068371/html/index.html
Slide 5 : General Definition of Medical Error The failure of a planned action to be completed as intended The use of a wrong action to achieve an aim The failure to apply an action that is indicated as the standard of care.
Slide 6 : Classification of Medical Errors Diagnostic Error or delay in diagnosis Failure to use indicated tests Use of outmoded tests or therapy Failure to act on results of monitoring or testing Treatment Error in performance of an operation, procedure or test Error in administering the treatment Error in the dose or method of using a drug Avoidable delay in treatment or in response to an abnormal test Inappropriate (not indicated) care Preventive Failure to provide prophylactic treatment Inadequate monitoring or follow-up of treatment Other Failure of communication Equipment failure Other system failure Leape et al. Preventing Medical Injury. Qual Rev Bull 19(5):144-149, 1993.
Slide 7 : Primary Studies 30,000 discharges from 51 NY state hospitals 3.7% adverse events Drug complications 19% Wound infections 14 % Technical complications 13% 13.6% of those led to death 15,000 discharges in Colorado and Utah 2.9% adverse events 6.6% of those lead to death More than 50% of adverse events were preventable Adverse events were noted as such in these charts Leape et al. N Engl J Med 324(6):377-384, 1991. Gawande et al. Surgery 126:66-75, 1999.
Slide 8 : Representative Supporting Studies Detailed chart reviews 815 medical patients at a university hospital 36% suffered iatrogenic illness 9% life-threatening or significant disability 1047 ICU/surgical patients at a teaching hospital 46% had "inappropriate decisions" made 18% had death or disability as a result Risk of adverse events increased 6% per day
Slide 9 : Analysis and Recommendations Death rates suggest medical error is the 8th leading cause of death Greater than automobile accidents, breast cancer or AIDS Cost est. 17-29 billion yearly (50% as heathcare)1 New drugs and information will increase the possibility of error in the future Error rate is intolerable and should be cut 50% in 5 years Recommendations Establish national Center for Patient Safety to set goals and track progress Develop research agenda related to medical error Develop national system for error reporting in both hospital and ambulatory settings Create performance standards for patient safety (required for accreditation and licensing) Error reporting and analysis should become an integral part of medical practice Information systems are a crucial component of error-free medical practice 1Thomas et al. JAMA 267:2487-2492, 1992.
Slide 10 : Error and Medical Culture Medicine is practiced in a complicated, distracting and error prone environment and error is avoided through extraordinary diligence Medicine has been practiced as a cottage industry with highly fragmented lines of authority (not standardized) Workflows often develop in an ad hoc manner Reimbursement has not incentivized error reduction Error may not produce outcomes that are clearly distinguishable as outliers Response to error has been to assign blame and individual penalty
Slide 11 : Medical Processes are Sensitive to Error Medicine has been classified in error research as a complex, tightly-coupled system Jet airliners, chemical refineries, nuclear power plants Events are highly dependent on multiple other events and temporally-linked, result of error not easy to predict, individual events may dramatically impact outcome Error propagates easily and induces additional error Contrast with linear, loosely-coupled systems Error in complex systems responds best to development of work processes that are less error-prone rather than attempts to improve individual performance
Slide 12 : Human Error
Slide 13 : Types of Human Error Mistakes Knowledge error: lack of knowledge or information ~ 11%, rarely recognized Lapses Rule error: failure to apply information correctly ~ 27%, occasionally recognized Slips Skill error: poor performance through distraction, inattention, inadequate skill ~ 60%, commonly recognized
Slide 14 : Reasons for Human Error People react to current conditions by automatically matching them to patterns in memory by similarity and frequency Pattern-matching is fast but not perfect (vs. thinking) Error is a byproduct of normal mental processes and can be reduced, but not eliminated, by education and training Error can be increased by environmental characteristics Distraction, interruption Presentation of different situations in similar or misleading ways Error can be anticipated and decreased by environmental safeguards (procedural checks or backup systems)
Slide 15 : Risky Work Processes and Human Error Active failures Committed by people Immediate effect Latent conditions: “risky,” promote error Characteristic of the workflow or system design Combine with local circumstances (triggers) to promote, propagate, or intensify errors May cause error intermittently, or lie dormant for extended periods Can be identified and eliminated proactively A bad outcome usually results from a complex coincidence of latent errors and active failures
Slide 16 : Propagation of Error Latent Error Human Error Human Error Latent Error Human Error Monitoring System Accident X
Slide 17 : Decreasing Error
Slide 18 : Decreasing Error Reporting and follow-up Other strategies for limiting error Role of information systems
Slide 19 : Reporting Systems for Errors Collect as many errors as possible to detect patterns indicating latent errors (“sentinal events”) Non-punitive Scapegoating (assigning fault and blame) strongly inhibits error reporting Confidential Air traffic reporting system de-identifies reports after confirmation Simple to use Should provide feedback on outcome Should collect “near misses” in addition to errors
Slide 20 : The Importance of “Near-Misses” Definition: Unintended (“risky”) events that did not result in patient harm or increased cost of care Near misses are much more common than errors/accidents with negative outcomes Other complex systems with excellent safety records (airlines, nuclear reactors, refineries) avidly collect and analyze near misses An important source of data for error prevention Near misses in medicine are difficult to identify/analyze
Slide 21 : Analyzing and Responding to Error Identify “sentinal events” “Root cause” analysis (why, not who) Cross-disciplinary Temporal reconstruction of events to identify all involved errors and risky elements Classification of errors Identification of the error propagation path Identification of other possible propagation patterns Proposal of system modifications to limit error and error propagation
Slide 22 : Strategies for Limiting Error Adequate training Accessible information for decisions or reference Support systems fit workflow Avoid too many rules Balance productivity and safety (can be challenging) Provide error defenses (process monitoring and alert systems) as needed Catalog problems and risky situations to find and weed out latent errors
Slide 23 : Information Systems and Medical Error Identification of error for follow-up Order surveillance with immediate alerting Access to reference information Rule-based process monitor systems (Examples with Drug Therapy)
Slide 24 : Drug Therapy and Medical Error Most common cause of iatrogenic injury (20%) Serious or life-threatening in 42% ~40% of those preventable 56% related to physician ordering Adds costs of $1900 - $5900 per episode Excludes subsequent treatment or malpractice awards More common in complex patients, elderly and pediatrics Associated with poor information access, guideline noncompliance, inadequate monitoring; more likely when individualization of therapy is required
Slide 25 : Detection of Adverse Drug Events Voluntary reporting of ADE relatively low Chart review vs. computer monitoring Overlap on only 12% of identified cases Computer monitoring: lab value patterns (discrete data elements) Chart review: physical symptoms from textual progress notes Manual monitoring requires five-fold more time Approaches to date designed to identify errors of commission in limited data sets Jha, et al. JAMIA 5(3):305-314, 1998.
Slide 26 : Prevention of Drug Therapy Errors Rules that detect combinations of pharmacy orders, laboratory values, other data elements (problem lists, allergies) PPV of 23% to 56%; sensitivity 45%, specificity 79% (lab values) CPOE: computerized physician order entry Rule-based surveillance with physician alerting at order time Reduction in adverse drug reactions of 55-81% with POE/alerting Classen, et al. JAMA 266(20):2847-2851, 1991. Bates, et al. JAMIA 6(4):313-312, 1999. Theurmann et al. Drug Saf 25:713-24, 2002
Slide 27 : Potential Benefits of Information Systems Ubiquitous access to all relevant data at decision time and place Real-time patient status alerts Data displayed in appropriate context Organization and sequence of data displayed is appropriate for decision-making Assumes high quality system implementation!
Slide 28 : Problems with Information Systems
Slide 29 : Effect of Hospital “Computerization” on Physician Performance Review of 100 studies: computer systems improved performance when reports were written by the system developers1 When the authors were not the developers, physician performance improved in only 28% of studies1 But “computerization” probably can’t be regarded as a single intervention for analysis Computerization did not clearly reduce overall ADEs2 Physician errors in prescribing were not reduced by a system that provided online ordering without decision support2 Other studies have shown substantial benefit when decision support was provided 1Garg et al. JAMA 293(10)1223-1238, 2005 2Nebeker et al. Arch Intern Med 165:1111-1116, 2005
Slide 30 : Information Systems Create New Opportunities for Error Medical processes are complex, are often not completely understood and are evolving Workflows and decision requirements are difficult to capture in software Tension between regulations, current practices and available systems Systems supporting medical processes are complex and interact in complex ways with... Users (may require or allow workflow changes) Other information systems Non-automated processes Information availability not always improved New latent errors are likely introduced that will required diligent follow-up to identify and correct
Slide 31 : An Example Case Horsky et al. JAMIA 12:377-382, 2005
Slide 32 : Case Description Patient transferred from ICU to pulmonary floor Physician A sees low K of 3.1 meq/l with renal insufficiency and orders 40 meq KCl over 4 hr in CPOE system ‘A’ finds that patient already has IV line and KCl can be added to current IV fluid ‘A’ enters new order for 100 meq of KCl in 1 L D5W at 75 ml/hr. ‘A’ discontinues bolus IV KCl order from two days previously rather than the one recently entered Pharmacy calls ‘A’ to let him know that 100 meq of KCl is higher than their guidelines
Slide 33 : Case Description (2) ‘A’ discontinued the order for 100 meq KCl ‘A’ entered a new order for 80 meq/L of KCL but failed to include an absolute volume of fluid or stop time (the intent was a total volume of 1 L). The patient received the first bolus of 40 meq/L KCl and also received 80 meq/L KCl at 75 ml/hr for 36 hours. Thus the patient received 256 meq KCl over 36 hr. On Sunday morning there was a coverage change. ‘A’ notified the incoming physician ‘B’ to check the patient’s K level.
Slide 34 : Case Description (3) ‘B’ check the patient’s K in the computer, which was the previous low value of 3.1. He did not realize that this value was obtained prior to therapy. ‘B’ ordered an additional 60 meq KCl IV while the existing KCl IV infusion was still running. ‘B’ ordered another 40 meq KCl IV 30 min later, but the record did not show that it was administered. The patient received a total of 316 meq KCl over 42 hr. On Monday morning, a new K evaluation showed severe elevation in K (7.8 meq/l) and the patient was treated for hyperkalemia.
Slide 35 : Key Issues In the CPOE application, IV drip fluid orders can only be specified by length of infusion time, not total volume, with a default of 7 days. Drips run for the indicated time irrespective of volume, even though a “total volume” figure is shown on screen. IV injection/bolus order screens are very similar in appearance to drip fluid orders, but are specified by duration and dose. The automated calculation of stop times is significantly different between drips and injections/bolus infusions. A text request to limit the infusion to 1 L was entered into a text comment field. The system’s time and amount checking functions respond only to coded entries, not free text. IV fluids are not displayed on the medication list (even if they contain medications). ‘B’ was not notified that the patient had an ongoing KCl drip because ‘A’ though the drip had ended after 1 L. The lab data display screen shows the most recent labs. Although dates/times are shown, it does not clearly indicate when the most recent lab is not current. The pharmacy system could detect orders with too high a KCl concentration, but not when KCl was infused for too long or when to much was given by multiple routes (may lead to a false sense of security). Inadequate CPOE training (evidence of trial-and-error entry of orders).
Slide 36 : Summary Error is relatively well-understood and has been dealt with effectively in many industries Medical error is common, frequently serious, and methods for dealing with it are relatively poorly developed Medical processes are as complicated and are more variable than engineering processes Error is more difficult to identify Error propagation is more difficult to predict Error reporting/identification methods need development Information systems should help limit error, but must be correctly designed and implemented I.S. are complex and connect with care processes in complex ways, creating opportunities for introducing latent error

 


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